Application of computational chemistry in chemical reactivity: a review

C. W. Chidiebere; C. E.  Duru; JP. C.  Mbagwu

doi:10.46481/jnsps.2021.347

Authors

C. W. Chidiebere
[email protected]
Department of Chemistry, Imo State University, Surface Chemistry and Environmental Technology (SCENT) Research Unit, Department of Chemistry, Imo State University, Owerri, Imo State, Nigeria
C. E. Duru Department of Chemistry, Imo State University, Surface Chemistry and Environmental Technology (SCENT) Research Unit, Department of Chemistry, Imo State University, Owerri, Imo State, Nigeria
JP. C. Mbagwu Department of Physics, Faculty of Physical Sciences, Imo State University, Owerri, Nigeria.

Keywords:

HOMO-LUMO, Computational methods , Chemical reactivity

Abstract

Molecular orbitals are vital to giving reasons several chemical reactions occur. Although, Fukui and coworkers were able to propose a postulate which shows that highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) is incredibly important in predicting chemical reactions. It should be kept in mind that this postulate could be a rigorous one therefore it requires an awfully serious attention in order to be understood. However, there has been an excellent breakthrough since the introduction of computational chemistry which is mostly used when a mathematical method is fully well built that it is automated for effectuation and intrinsically can predict chemical reactivity. At the cause of this review, we’ve reported on how HOMO and LUMO molecular orbitals may be employed in predicting a chemical change by the utilization of an automatic data processing (ADP) system through the utilization of quantum physics approximations.

Dimensions

REFERENCES

L. S. Braga, D. H. Leal, K. Kuca, & T. C. Ramalho, “Perspectives on the Role of the Frontier Effective-for-Reaction Molecular Orbital (FERMO) in the Study of Chemical Reactivity”, An Updated Review Current Organic Chemistry 24 (2020) 314.

D. S.Taylor & A. Holewinski, “Selective Interactions between Free-Atom-like d-States in Single-Atom Alloy Catalysts and Near-Frontier Molecular Orbitals”, Journal of the American Chemical Society 13 (2021) 342.

F. Guohong, Q. Wang, X. Hong, X. Wang, T. Xianxian, & C. Xiangfeng, “Single Cr atom supported on boron nitride nanotubes for the reaction of N2O reduction by CO: A density functional theory study”, Applied Surface Science 544 (2021) 148776.

F. Wang, L. Zhongling, L. Yan, S. Alessandro, J. M. Poblet, & C. de Graaf, “Density functional theory study of single-molecule ferroelectricity in Preyssler-type polyoxometalates”, APL Materials 9 (2021) 021109.

X. Wei, M. Miaomiao, H. Zhang, L. Wang, & C. Dong, “Density functional theory study of ultra-thin In Se electrodes for Na and Mg ion storage and transport”, Materials Letters 285 (2021) 129091.

M. Itakura, M. Yamaguchi, D. Egusa, & E. Abe, “Density functional theory study of solute cluster growth processes in Mg-Y-Zn LPSO alloys”, Acta Materialia 203 (2021) 116491.

L. Shi, Y. Huang, L. Zhang-Hui, C. Wanglai, Y. Xiaohu, S. Qing, Z. Gao, R. Zhang, & G. Feng, “Surface property of the Cu doped -Al2O3: A density functional theory study” Applied Surface Science 535 (2021) 147651.

P. Chen, G. Mingyan, D. Wang, J. Wang, X. Huang, H. Wang, & Y. Lin, “Experimental and density functional theory study of the influence mechanism of oxygen on NO heterogeneous reduction in deep air-staged combustion”, Combustion and Flame 223 (2021) 127.

K. Park, J. Byeong-Hyeon, H. Y. Lim, & P. Ji-Sang, “Effect of chemical substitution on polytypes and extended defects in chalcopyrites: A density functional theory study”, Journal of Applied Physics 129 (2021) 025703.

B. B. Xiao, L. Yang, H. Y. Liu, X. B. Jiang, B. Aleksandr, E. H. Song, & Q. Jiang, “Designing fluorographene with FeN4 and CoN4 moieties for oxygen electrode reaction: A density functional theory study”, Applied Surface Science 537 (2021) 147846.

A. Liu, W. Guan, W. Kefan, X. Ren, L. Gao, & M. Tingli, “Density functional theory study of nitrogen-doped graphene as a high-performance electrocatalyst for CO2RR”, Applied Surface Science 540 (2021) 148319.

Y. Yang, A. Sun, & M. Eslami, “A density functional theory study on detection of amphetamine drug by silicon carbide nanotubes”, Physica E: Low-dimensional Systems and Nanostructures 125 (2021) 114411.

R. Hussain, S. Muhammad, M. Y. Mehboob, S. U. Khan, M. U. Khan, M. Adnan, M. Ahmed, J. Iqbal, & K. Ayub, “Density functional theory study of palladium cluster adsorption on a graphene support”, RSC Advances 10 (2020) 20595.

A. S. Shakeel & D. C. Gupta, “Investigation of structural, elastic, thermophysical, magneto-electronic, and transport properties of newly tailored Mn-based Heuslers: A density functional theory study”, International Journal of Quantum Chemistry 120 (2020) 26216.

Z. Jafari, R. Baharfar, A. R. Shokuhi, & S. Asghari, “Potential of graphene oxide as a drug delivery system for Sumatriptan: a detailed density functional theory study”, Journal of Biomolecular Structure and Dynamics 39 (2020) 1.

J. Keunhong, H. J. Jeong, L. Gunwoo, S. H. Kim, H. K. Kwang, & G. Y. Chang, “Catalytic effect of alkali and alkaline earth metals in lignin pyrolysis: A density functional theory study”, Energy & Fuels 34 (2020) 9734.

P. Zhao, B. Boekfa, S. Ken-ichi, M. Ogura, & M. Ehara, “Selective catalytic reduction of NO with NH 3 over Cu-exchanged CHA, GME, and AFX zeolites: a density functional theory study”, Catalysis Science & Technology 11 (2021) 1780.

C. Yuan, C. Zhang, Y. Shenghui, H. Xu, L. Xin, Q. Fang, & G. Chen, “Experimental and Density Functional Theory Study of the Adsorption Characteristics of CaO for SeO2 in Simulated Flue Gas and the Effect of CO2”, Energy & Fuels 34 (2020) 10872.

M. Masoumi, M. Jahanshahi, M. G. Ahangari, G. & N. Darzi, “Density functional theory study on the interaction of chitosan monomer with TiO 2 , SiO 2 and carbon nanotubes”, Materials Chemistry and Physics 255 (2020) 123.

Z. Lyu, S. Niu, L. Chunmei, G. Zhao, Z. Gong, & Y. Zhu, “A density functional theory study on the selective catalytic reduction of NO by NH3 reactivity of ?-Fe 2 O 3 (0 0 1) catalyst doped by Mn, Ti, Cr and Ni”, Fuel 267 (2020) 117.

O. Zhiliang, R. Jingyu, N. Juntian, Q. Changlei, H. Wei, & L. Yang, “A density functional theory study of CO2 hydrogenation to methanol over Pd/TiO2 catalyst: The role of interfacial site”, International Journal of Hydrogen Energy 45 (2020) 6328.

A. A. Peyghan & J. Beheshtian, “Application of hexa-peri-hexabenzocoronene nanographene and its B, N, and Bn doped forms in Na-ion batteries: A density functional theory study”, Thin Solid Films 704 (2020) 137.

H. Li, Z. Zhang, Y. Liu, C. Wanglai, & X. Luo, “ Functional group effects on the HOMO-LUMO gap of g-C3N4 Nanomaterials” 8 (2018) 589, S. R. Wang, M. Arrowsmith, J. Böhnke, H. Braunschweig, T. Dellermann,

R. D. Dewhurst, H. Kelch, I. Krummenacher, J. D. Mattock, J. H. Müssig, T. Thiess, A. Vargas, & J. Zhang, “Engineering a Small HOMO-LUMO Gap and Intramolecular C-H Borylation by Diborene/Anthracene Orbital Intercalation”, Angewandte Chemie International Edition 56 (2017) 8009.

C. Dongping & H. Wang, “HOMO-LUMO energy splitting in polycyclicaromatic hydrocarbons and their derivatives”, Proceedings of the Combustion Institute 37 (2019) 953.

Y. Huang, C. Rong, R. Zhang, & S. Liu, “Evaluating frontier orbital energy and HOMO/LUMO gap with descriptors from density functional reactivity theory”, Journal of molecular modeling 23 (2017) 1.

Y. Morisawa, S. Tachibana, A. Ikehata, T. Yang, M. Ehara, & Y. Ozaki, “Changes in the Electronic States of Low-Temperature Solid n-Tetradecane: Decrease in the HOMO-LUMO Gap”, ACS omega 2 (2017) 618.

D. Chen & H. Wang, “Homo-lumo gaps of homogeneous polycyclic aromatic hydrocarbon clusters”, The Journal of Physical Chemistry 123 (2019) 27785.

R. Gershoni-Poranne, A. P. Rahalkar, & A. Stanger, “The predictive power of aromaticity: quantitative correlation between aromaticity and ionization potentials and HOMO-LUMO gaps in oligomers of benzene, pyrrole, furan, and . . . ”, Physical Chemistry Chemical Physics 20 (2018) 14808.

A. B. Marahatta, “Computational Study on Electronic Structure, Atomic Charges Distribution and Frontier Molecular Orbitals of Butadiene: General Features for Diels-Alder Reaction”, International Journal of Progressive Sciences and Technologies 19 (2020) 48.

F. A. Bulat, J. S. Murray, & P. Politzer, “Identifying the most energetic electrons in a molecule: The highest occupied molecular orbital and the average local ionization energy”, Computational and Theoretical Chemistry 1199 (2021) 113192.

P. Vinduja, V. K. Rajan, S. Krishna, & K. Muraleedharan, “A Computational Modeling of the Structure, Frontier Molecular Orbital (FMO) Analysis, and Global and Local Reactive Descriptors of a Phytochemical ‘Coumestrol’ ”, Mathematics Applied to Engineering in Action (2021) 41.

K. Harismah, A. M. Dhumad, H. S. Ibraheem, H. Zandi, & H. J. Majeed, “A DFT approach on tioguanine: Exploring tio-tiol tautomers, frontier molecular orbitals, IR and UV spectra, and quadrupole coupling constants”, Journal of Molecular Liquids 334 (2021) 116018.

D. Baelde, S. Delaune, C. Jacomme, A. Koutsos, & S. Moreau, “An interactive prover for protocol verification in the computational model”, SP 2021-42nd IEEE Symposium on Security and Privacy.

E. M. Heffernan, J. D. Adema, & M. L. Mack, “Identifying the neural dynamics of category decisions with computational model-based functional magnetic resonance imaging”, Psychonomic Bulletin & Review (2021) 1.

M. Abadi, M. Isard, & D. G. Murray, “A computational model for TensorFlow: an introduction Proceedings of the 1st ACM SIGPLAN”, International Workshop on Machine Learning and Programming Languages (2017) 1.

G. B. Goh, N. O. Hodas, & A. Vishnu, “Deep learning for computational chemistry”, Journal of computational chemistry 38 (2017) 1291.

S. McArdle, S. Endo, A. Aspuru-Guzik, S. C. Benjamin, & X. Yuan, “Quantum computational chemistry”, Reviews of Modern Physics 92 (2020) 015003.

L. Ling, F. Maohong, B. Wang, & R. Zhang, “Application of computational chemistry in understanding the mechanisms of mercury removal technologies”, a review Energy & Environmental Science 8 (2015) 3109.

D. L. Monego, M. Barcellos da Rosa, & P. C??cero do Nascimento, “Applications of computational chemistry to the study of the antiradical activity of carotenoids”, A review Food chemistry 217 (2017) 37.

P. Gatt, R. Stranger, & R. J. Pace, “ Application of computational chemistry to understanding the structure and mechanism of the Mn catalytic site in photosystem II”, - a review Journal of Photochemistry and Photobiology B: Biology 104 (2011) 80.

E. Soriano & I. Fernandez, “Allenes and computational chemistry: from bonding situations to reaction mechanisms”, Chemical Society Reviews 43 (2014) 3041.

L. Ziheng, “Computational discovery of energy materials in the era of big data and machine learning”, a critical review Materials Reports: Energy (2021) 100047.

J. A. Keith, V. Vassilev-Galindo, B. Cheng, S. Chmiela, M. Gastegger, M. Klaus-Robert, & A. Tkatchenko, “Combining machine learning and computational chemistry for predictive insights into chemical systems arXiv [preprint] (2021)”, arXiv: (2102.06321).

P. Abhik, A. Sarkar, S. Saha, A. Maji, P. Janah, T. M. Kumar, “Synthetic and computational efforts towards the development of peptidomimetics and small-molecule SARS-CoV 3CLpro inhibitors”, a review Bioorganic & Medicinal Chemistry (2021) 116301.

T. T. Huong, N. V. Tran, A. K. Tieu, H. Zhu, H. Yu, & D. T. Thi, “Computational Tribochemistry: A Review from Classical and Quantum Mechanics Studies”, The Journal of Physical Chemistry 34 (2021) 1004.

H. Lei, L. Bai, D. D. Dionysiou, Z. Wei, R. Spinney, C. Chu, L. Zhang, & R. Xiao, “Applications of computational chemistry, artificial intelligence, and machine learning in aquatic chemistry research”, Chemical Engineering Journal 56 (2021) 131810.

H. L. Peña, D. A. Boateng, S. McPherson, & K. M. Tibbetts, “Using computational chemistry to design pump-probe schemes for measuring radical cation dynamics”, Physical Chemistry Chemical Physics 76 (2021) 205.